算法的迭代创新 在大模型训练这个系列里,我们将一起探索学习几种经典的分布式并行范式,包括流水线并行(Pipeline Parallelism),数据并行(Data Parallelism)和张量并行(Tensor Parallesim)。微软开源的分布式训练框DeepSpeed,融合了这三种并行范式,开发出3D并行的框架,实现了千亿级别模型参数的训练。本篇文章将探索流水线并行,...
算法的迭代创新 几种经典的分布式并行范式,包括流水线并行(Pipeline Parallelism),数据并行(Data Parallelism)和张量并行(Tensor Parallesim)。 微软开源的分布式训练框DeepSpeed,融合了这三种并行范式,开发出3D并行的框架,实现了千亿级别模型参数的训练。 经典的流水线并行范式有Google推出的Gpipe, 微软推出的PipeDream。 ...
[ LLM 分布式训练系列 02 ] 流水线并行(Pipeline Parallelism)- GPipe 在LLM 分布式训练这个系列,我打算记录一下目前主要的几种并行方法: 流水线并行(Pipeline Parallelism) 数据并行(Data Parallelism) 张量并行(Tensor Parallelism) 本篇文章以 Google 在 2019 年推出的 GPipe [1] 为例,介绍下流水线并行的原理。
在训练大模型的过程中,“模型并行”是一种绕不开的核心技术。 本期视频将带你通俗易懂地搞懂 Tensor 并行(Tensor Parallelism) 和 Pipeline 并行(Pipeline Parallelism) 的基本原理、实现方式以及它们之间的区别和应用场景。, 视频播放量 81、弹幕量 0、点赞数 3、投硬
Hey vllm team, Hope you're all doing great! I‘m focusing on pipeline parallel inference and I hope it can be support on vllm. I noticed that pipeline parallelism was on the old roadmap(#244) , but it's not on the new roadmap(#2681). Just...
Pipeline Parallelism for PyTorch. Contribute to pytorch/PiPPy development by creating an account on GitHub.
data parallelism across multi-GPU servers with a novel interleaved pipelining scheduling strategy, increasing the throughput by more than 10%. Recently, Colossal-AI[111]implemented a combination of various data, pipeline, sequence, and multiple tensor parallelism for large-scale model training, which ...
Contains implementations for the various point-to-point communication needed (e.g.,recv_forwardandrecv_backward) in the different pipeline parallelism schedules. core.pipeline_parallel.p2p_communication.recv_backward(tensor_shape:Union[List[int],torch.Size],config:megatron.core.ModelParallelConfig)→ tor...
All Nemotron-4 340B models are optimized with TensorRT-LLM to take advantage of tensor parallelism, a type of model parallelism in which individual weight matrices are split across multiple GPUs and servers, enabling efficient inference at scale. ...
Disabling tokenizer parallelism, we're using DataLoader multithreading already [{'label': 'POSITIVE', 'score': 0.9998525381088257}, {'label': 'NEGATIVE', 'score': 0.9997695088386536}] ''' # 传入原始字符串生成器 def list_to_generator(lst): ...